# What talking you?: Translating Code-Mixed Messaging Texts to English

arXiv: https://arxiv.org/abs/2411.05253 · HTML: https://mlsanity.com/paper/2411.05253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [YaRN: Efficient Context Window Extension of Large Language Models](https://mlsanity.com/paper/2309.00071/) (2023)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://mlsanity.com/paper/2309.04766/) (2023)
- [From LLM to NMT: Advancing Low-Resource Machine Translation with Claude](https://mlsanity.com/paper/2404.13813/) (2024)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models](https://mlsanity.com/paper/2204.08398/) (2022)
- [Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017](https://mlsanity.com/paper/1803.06745/) (2018)
- [Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation](https://mlsanity.com/paper/2408.12780/) (2024)
- [CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing](https://mlsanity.com/paper/2106.06004/) (2021)
- [Code-Mixer Ya Nahi: Novel Approaches to Measuring Multilingual LLMs' Code-Mixing Capabilities](https://mlsanity.com/paper/2410.11079/) (2024)
- [Disentangling Singlish Discourse Particles with Task-Driven Representation](https://mlsanity.com/paper/2409.20366/) (2024)
- [Code-mixed Sentiment and Hate-speech Prediction](https://mlsanity.com/paper/2405.12929/) (2024)
- [IKUN for WMT24 General MT Task: LLMs Are here for Multilingual Machine Translation](https://mlsanity.com/paper/2408.11512/) (2024)

## Built on by (most cited first, 4 of 4)

- [Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text](https://mlsanity.com/paper/2506.14012/) (2025)
- [Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation](https://mlsanity.com/paper/2507.11966/) (2025)
- [Stylistic Evolution and LLM Neutrality in Singlish Language](https://mlsanity.com/paper/2601.06580/) (2026)
- [The Hrunting of AI: Where and How to Improve English Dialectal Fairness](https://mlsanity.com/paper/2603.15187/) (2026)

## Abstract (arXiv)

Translation of code-mixed texts to formal English allow a wider audience to understand these code-mixed languages, and facilitate downstream analysis applications such as sentiment analysis. In this work, we look at translating Singlish, which is colloquial Singaporean English, to formal standard English. Singlish is formed through the code-mixing of multiple Asian languages and dialects. We analysed the presence of other Asian languages and variants which can facilitate translation. Our dataset is short message texts, written as informal communication between Singlish speakers. We use a multi-step prompting scheme on five Large Language Models (LLMs) for language detection and translation. Our analysis show that LLMs do not perform well in this task, and we describe the challenges involved in translation of code-mixed languages. We also release our dataset in this link https://github.com/luoqichan/singlish.